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Implementation of an AI-Powered Wearable for Remote Patient Surveillance
Subject area: Science,Engineering and Technology · Area of research: Machine Learning(ML) and Internet of Things(IoT)
DOI: https://doi.org/10.64388/IREV9I10-1715879
Abstract
This article focuses on the design and implementation of a wearable AI-driven gadget for remote patient monitoring. It helps overcome the common obstacles in the traditional healthcare system, where timely action, accessibility, and constant supervision are always a challenge. With the integration of the Internet of Things, embedded systems, and AI, this wearable facilitates round-the-clock health monitoring. The hardware implementation uses a Seeed Studio XIAO ESP32-S3 microcontroller with a MAX30102 sensor for heart rate and SpO₂, measurements, an OLED display for real-time feedback, a buzzer for notifications, and a LiPo battery for portability. The software implementation involves embedded systems, Node.js, and PostgreSQL with real-time communication using Socket.IO. An LSTM neural network is used for anomaly detection and predictive health classification. Through experimental evidence, real-time surveillance is successfully performed with a latency of below 2 seconds and a model accuracy of 97%, and an accuracy of overlooking critical events of 99%. It was observed, however, that there were challenges like sensor instability in movement and limited battery life. The research finds that AI-based wearable technologies can be used to deliver scalable healthcare, especially in resource-limited settings, but need additional optimization and clinical trials.
Keywords
AI, Wearable Health Devices, Remote Patient Monitoring, IoT, LSTM, Edge Computing.
References
[1] Arpaia, P., Crauso, F., De Benedetto, E., Duraccio, L., Improta, G., & Serino, F. (2022). Soft transducer for patient’s vitals telemonitoring with deep learning-based personalized anomaly detection. Sensors, 22(2), 536.
[2] Arpaia, P., Moccaldi, N., Prevete, R., Sannino, I., & Tedesco, A. (2020). A wearable EEG instrument for real-time frontal asymmetry monitoring in worker stress analysis. IEEE Transactions on Instrumentation and Measurement, 69(10), 8335- 8343.
[3] Bennett, R., Hemmati, M., Ramesh, R., & Razzaghi, T. (2024). Artificial intelligence and machine learning in precision health: an overview of methods, challenges, and future directions. Dynamics of Disasters: From Natural Phenomena to Human Activity, 15-53.
[4] Bignami, E. G., Fornaciari, A., Fedele, S., Madeo, M., Panizzi, M., Marconi, F., Cerdelli, E., & Bellini, V. (2025). Wearable Devices in Healthcare Beyond the One-Size-Fits-All Paradigm. Sensors, 25(20), 6472. https://
[5] Karthika, M., Abraham, J., Kodali, P. B., & Mathews, E. (2025). Emerging trends of chronic diseases and their care among older persons globally. Handbook of aging, health and public policy: perspectives from Asia, 641-664.
[6] Martínez, A. V. (2026). Intelligent Automatic Feeder for Pregnant Sows. International Journal of Combinatorial Optimization Problems and Informatics, 17(2), 174.
[7] Mbunge, E., Muchemwa, B., Jiyane, S. E., & Batani, J. (2021). Sensors and healthcare 5.0: transformative shift in virtual care through emerging digital health technologies. Global Health Journal, 5(4), 169-177.
[8] Romagnoli, R., Civitella, S., Minganti, C., & Piacentini, M. F. (2022). Concurrent and predictive validity of an exercise-specific scale for the perception of velocity in the back squat. International Journal of Environmental Research and Public Health, 19(18), 11440.
[9] Rosca, C. M., & Stancu, A. (2025). Anomaly Detection in Elderly Health Monitoring via IoT for Timely Interventions. Applied Sciences, 15(13), 7272.
[10] Sanchez-Martinez, S., Camara, O., Piella, G., Cikes, M., González-Ballester, M. Á., Miron, M., ... & Bijnens, B. (2022). Machine learning for clinical decision-making: challenges and opportunities in cardiovascular imaging. Frontiers in cardiovascular medicine, 8, 765693.
[11] Sánchez-Reolid, R., López de la Rosa, F., Sánchez-Reolid, D., López, M. T., & Fernández- Caballero, A. (2022). Machine learning techniques for arousal classification from electrodermal activity: A systematic review. Sensors, 22(22), 8886.
[12] Wong, B. K. M., Law, F. L., & Bastrygina, T. (2025). IoT in healthcare: Trends, opportunities, and challenges. Digital Tools and Data for Innovative Healthcare, 299-322.
How to cite this paper
@article{1715879,
author = {Awe Omosigho Florence},
title = {Implementation of an AI-Powered Wearable for Remote Patient Surveillance},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {10},
pages = {746-751},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1715879.pdf},
abstract = {This article focuses on the design and implementation of a wearable AI-driven gadget for remote patient monitoring. It helps overcome the common obstacles in the traditional healthcare system, where timely action, accessibility, and constant supervision are always a challenge. With the integration of the Internet of Things, embedded systems, and AI, this wearable facilitates round-the-clock health monitoring. The hardware implementation uses a Seeed Studio XIAO ESP32-S3 microcontroller with a MAX30102 sensor for heart rate and SpO₂, measurements, an OLED display for real-time feedback, a buzzer for notifications, and a LiPo battery for portability. The software implementation involves embedded systems, Node.js, and PostgreSQL with real-time communication using Socket.IO. An LSTM neural network is used for anomaly detection and predictive health classification. Through experimental evidence, real-time surveillance is successfully performed with a latency of below 2 seconds and a model accuracy of 97%, and an accuracy of overlooking critical events of 99%. It was observed, however, that there were challenges like sensor instability in movement and limited battery life. The research finds that AI-based wearable technologies can be used to deliver scalable healthcare, especially in resource-limited settings, but need additional optimization and clinical trials.},
keywords = {AI, Wearable Health Devices, Remote Patient Monitoring, IoT, LSTM, Edge Computing.},
month = {April},
doi = {https://doi.org/10.64388/IREV9I10-1715879}
}